Extensible Behavioral Risk Framework With Reusable ML Models
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Solution Overview
Problem
Existing risk-detection systems require training and maintaining multiple, unique machine learning models for each type of risk, leading to inefficiencies in resource utilization and maintenance burden, as they struggle to adapt to new types of risks.
Innovation Solution
A framework that enables model reuse and composability by combining machine learning models and lexicons to detect violations across various risk types, allowing for rapid expansion of cognitive scenario catalogs and improved analytic outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distinct machine learning models are trained for each type of risk, then detection accuracy for specific risk types is improved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent applies universality by designing a single machine learning model framework that can detect multiple types of risks across different domains. The model is trained on diverse data including communications, transactions, and events, enabling it to perform multiple detection functions simultaneously rather than requiring separate specialized models for each risk type.
Solution Approach 2:
The patent merges multiple risk detection capabilities into a unified system. By combining various data sources (communications, transactions, events) and training a single model on this diverse dataset, the system consolidates what would traditionally require multiple separate models into one integrated framework, reducing overall system complexity.
2Adaptability or versatility
If multiple unique machine learning models are maintained for different risk types, then comprehensive risk coverage is achieved, but loss of time and resources for training and maintenance increases
Solution Approach 1:
The unified model framework provides comprehensive risk coverage across multiple domains simultaneously. By designing the model to handle diverse risk types from the outset and training it on multi-domain data, the system achieves broad adaptability without requiring separate models for each risk category, thereby eliminating the time and resources needed to train and maintain multiple specialized models.
3Adaptability or versatility
If the system adapts to detect new types of risks, then adaptability improves, but device complexity and maintenance burden increase
Solution Approach 1:
The patent applies dynamics by creating a flexible, adaptable model framework that can evolve to detect new risk types. The system uses diverse training data and a unified architecture that can incorporate new risk categories without requiring structural changes or additional specialized models, allowing dynamic adaptation while maintaining system simplicity.
Data Source
AI summary
Some aspects of the present disclosure relate to systems, methods and computer readable media for outputting alerts based on potential violations of predetermined standards of behavior. In one example implementation, a computer implemented method includes: training a natural language-based machine learning model to detect at least one risk of a violation condition in an electronic communication between persons, wherein the violation condition is a potential violation of a first predetermined standard of behavior; receiving a lexicon, wherein the lexicon comprises topic data; receiving connection data representing a relationship between the trained machine learning model and the lexicon; detecting, using the trained machine learning model, the lexicon, and the connection data, a potential violation of a second predetermined standard of behavior; and outputting for display an alert indicating the potential violation of the second predetermined standard of behavior.